Posture based detection of attention in human computer interaction

Abstract

Unacted posture conveys cues about people's attentional disposition. We aim to identify robust markers of attention from posture while people carry out their duties seated in front of their computers at work. Body postures were randomly captured from 6 subjects while at work using a Kinect, and self-assessed as attentive or not attentive. Robust postural features exhibiting higher discriminative power across classification exercises with 4 well-known classifiers were identified. Average classification of attention from posture reached 76.47%±4.58% (F-measure). A total of 40 postural features were tested and those proxy of head tilt were found to be the most stable markers of attention in seated conditions based upon 3 class separability criteria. Unobtrusively monitoring posture of users while working in front of a computer can reliably be used to infer attentional disposition from the user. Human-computer interaction systems can benefit from this knowledge to customize the experience to the user changing attentional state. © 2014 Springer-Verlag Berlin Heidelberg.